Generating and suggesting phrases

ABSTRACT

Techniques for generating and providing phrases are described herein. These techniques may include analyzing one or more sources to generate a first corpus of phrases, each of the phrases for use as an identifier and/or for association with a user for executing a transaction. Once a first corpus of phrases has been generated, these phrases may be filtered to define a second corpus of phrases. Phrases of this second corpus may then be suggested to one or more users. In some instances, the phrases suggested to a particular user are personalized to the user based on information previously known about the user or based on information provided by the user.

RELATED APPLICATIONS

This application is related to U.S. Provisional Application No.60/823,611, filed on Aug. 25, 2006, and U.S. patent application Ser. No.11/548,111, filed on Oct. 10, 2006, both entitled UTILIZING PHRASETOKENS IN TRANSACTIONS and both incorporated herein by reference.

BACKGROUND

Companies utilizing e-commerce sites strive to make their sites easierfor users to locate and purchase items. In order to ease users' abilityto purchase items, for instance, these companies may configure theirsites to accept many forms of payment. While many of these strategieshave increased the effectiveness of these sites, companies continuallystrive to further enhance user experiences.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical items.

FIG. 1 illustrates an example architecture that includes aphrase-generation service for generating phrases. Once the phrases aregenerated, some of the phrases may be suggested to one or more users.

FIG. 2 illustrates another example architecture that includes thephrase-generation service of FIG. 1. Here, the architecture includes acontent provider that stores information about the users to whom thephrases are suggested.

FIG. 3 illustrates an example user interface served by the contentprovider of FIGS. 1 and 2. Here, the example user interface includesmultiple phrases that the phrase-generation service of FIGS. 1 and 2 hasgenerated.

FIG. 4 illustrates an example user interface after the user has enteredwords into a text box of the user interface from FIG. 3. As illustrated,in response to the entering of the words, the user interface nowincludes different phrases that are related to the entered words, aswell as an indication that the entered words are available as a phrase.

FIG. 5 illustrates another example user interface after the user hasentered words into a text box of the user interface from FIG. 3. Again,this user interface now includes different phrases that are related tothe entered words, as well as an indication that the misspelled word maynot be used as a part of a selected phrase.

FIG. 6 illustrates another example user interface after the user hasentered words into a text box of the user interface from FIG. 3. Again,this user interface now includes different phrases that are related tothe entered words, as well as an indication that the entered words havepreviously been selected as a phrase by another user.

FIG. 7 illustrates another example user interface served by the contentprovider of FIGS. 1 and 2. This interface illustrates each phrase thathas been associated with a particular user.

FIG. 8 illustrates example components of the phrase-generation serviceof FIGS. 1 and 2.

FIG. 9 illustrates an example flow diagram for generating and suggestingphrases.

FIG. 10 illustrates an example flow diagram for determining phrases thatare related to statistically improbable phrases, each of which comprisesa phrase that appears in a source more than a predetermined thresholdnumber of times.

FIG. 11 illustrates an example feedback loop for learningcharacteristics about the phrases selected by users and, in response tothe learning, altering the characteristics of phrases suggested tousers.

FIGS. 12-18 illustrate example processes for employing the techniquesdescribed below.

DETAILED DESCRIPTION

This disclosure is directed, in part, to generating phrases. Thesegenerated phrases may be for association with an entity, such as a useror a user account. These phrases may additionally or alternatively beoutput for use as identifiers. In one particular instance, a user mayselect a generated phrase for association with the user and with anaspect of a user account that is used to execute a payment transaction.For instance, the selected phrase may be associated with a paymentinstrument associated with the user account, a location (e.g., ashipping address or a digital location) associated with the useraccount, a delivery method associated with the user account, or anyother aspect of the user account. By associating the phrase in thismanner, the user may then use the phrase to conduct transactions withuse of the phrase. For instance, the user could purchase or receive anitem from a merchant with the use of the phrase, or the user couldsimilarly engage in any other sort of transaction with use of thephrase. As such, these generated phrases may operate similarly or thesame as the transaction phrase tokens described in the relatedapplications, incorporated by reference above.

In some instances, a phrase that is associated with a payment instrumentis free from information identifying the aspect of the user account,such as the payment instrument associated with user account. Therefore,the user associated with the phrase may more freely share the phrasethan an actual identifier of the payment instrument. That is, the usermay more freely share the phrase that is associated with the paymentinstrument when compared with the sharing of credit card numbers, bankaccount numbers, gift card numbers, and the like.

In some instances, the generated phrases comprise a set of numeric oralphanumeric characters having a secondary meaning to the user (e.g.,“Grace's Textbooks,” “Griffin's Toys,” etc.). Furthermore, in someinstances, each of the generated phrases may comprise at least one word.In still other instances, each of the phrases may comprise between twoand seven words, and may be free of numbers, symbols and the like. Assuch, these phrases may comprise a number of grammatically-correctwords.

To generate these phrases, the described techniques may analyze one ormore sources. These sources may include books, magazines, onlinecontent, audio content, video content and/or any other source from whichwords may be extracted or assembled. With use of the sources, thedescribed techniques may extract phrases or may extract words for use increating phrases.

Once the first corpus of phrases has been created by analyzing thesources, the techniques may filter the phrases to create a second corpusof phrases comprising fewer phrases than the first corpus of phrases.While the phrases may be filtered in any number of ways, in someinstances the techniques attempt to filter out phrases that users wouldperceive as less interesting, harder to remember, difficult topronounce, and/or the like. As such, the second corpus of phrases maycomprise phrases that make “good” phrases for selection. Statedotherwise, users may be more likely to perceive these phrases asinteresting, easier to remember, or the like as compared to thefiltered-out phrases.

After filtering and creation of the second corpus of phrases, thedescribed techniques may suggest phrases to one or more users. Theseusers may then, for instance, select one or more of these phrases forassociation with the user and, potentially, with a payment instrument ofthe user or another aspect of a user account. Conversely, these phrasesmay be used as a writing tool (e.g., to help an author with lyrics for asong, poem, or the like), as a naming tool (e.g., to help a user thinkof names for an entity, such as a band, business, restaurant, or thelike) or in any other manner where phrase generation may be helpful.

In some instances, the techniques suggest phrases that are personalizedto a user. For instance, the techniques may determine one or morekeywords that are associated with a user. These keywords may be manuallyinputted by a user or the keywords may be determined based on knowninformation about the user (e.g., name, address, purchase history,etc.). After determination of the keyword(s), the techniques maydetermine phrases of the second corpus of phrases that are associatedwith the keyword(s). After making this determination, the techniques maysuggest one or more of these phrases. For instance, if a user is knownto be a skier (and, hence, is associated with a keyword “ski”), thenphrases such as “powder hound,” “black diamond,” and “Winter Olympics”may be suggested to the user.

The discussion begins with a section entitled “IllustrativeArchitectures,” which describes two non-limiting environments in which aphrase-generation service generates and provides phrases for userselection. A section entitled “Illustrative User Interfaces” follows.This section depicts and describes examples of user interfaces (UIs)that may be served to and rendered at the devices of the users ofFIG. 1. Next, a section entitled “Illustrative Phrase-GenerationService” describes example components of a service that generates andprovides phrases for use in the illustrated architectures of FIGS. 1 and2 and elsewhere. A fourth section, entitled “Illustrative FlowDiagrams,” describes and illustrates processes for generating andsuggesting phrases, determining phrases that are related tostatistically improbable phrases, and altering suggested phrases withuse of a feedback loop. The discussion then concludes with a sectionentitled “Illustrative Processes” and a brief conclusion.

This brief introduction, including section titles and correspondingsummaries, is provided for the reader's convenience and is not intendedto limit the scope of the claims, nor the proceeding sections.Furthermore, the techniques described above and below may be implementedin a number of ways and in a number of contexts. Several exampleimplementations and contexts are provided with reference to thefollowing figures, as described below in more detail. However, thefollowing implementations and contexts are but a few of many.

Illustrative Architectures

FIG. 1 illustrates an example architecture 100 in which phrases may begenerated and suggested to one or more users. Users may then select oneor more of these phrases for use as identifiers or otherwise. In someinstances, a user may select a phrase for association with the user andwith one or more aspects of a corresponding user account. In someinstances, these one or more aspects may be helpful in allowing the useror another user to execute (e.g., initiate, complete, participate in, orotherwise aid) a payment transaction with use of the phrase. As such,the phrase may be associated with a payment instrument associated withthe user or user account, a location associated with the user or useraccount, or a delivery method associated with the user or user account.As such, the user or another entity may conduct transactions with use ofthe phrase, while any cost associated with the transaction may becharged to the underlying associated payment instrument. As discussed indetail below, the selected phrase may be free from informationidentifying the associated aspect of the user account (e.g., the paymentinstrument) and may, in some instances, comprise one or more words thatcollectively have a secondary meaning to the user.

In the illustrated embodiment, the techniques are described in thecontext of users 102(1), . . . , (N) operating computing devices 104(1),. . . , (N) to access a content provider 106 over a network 108. Forinstance, user 102(1) may use device 104(1) to access provider 106 forpurposes of consuming content offered by the content provider orengaging in a transaction with the content provider.

In architecture 100, content provider 106 may comprise any sort ofprovider that supports user interaction, such as social networkingsites, e-commerce sites, informational sites, news and entertainmentsites, and so forth. Furthermore, while the illustrated examplerepresents user 102(1) accessing content provider 106 over network 108,the described techniques may equally apply in instances where user102(1) interacts with the content provider over the phone, via a kiosk,or in any other manner. It is also noted that the described techniquesmay apply in other client/server arrangements, as well as innon-client/server arrangements (e.g., locally-stored softwareapplications, set-top boxes, etc.).

Here, user 102(1) accesses content provider 106 via network 108. Network108 may include any one or combination of multiple different types ofnetworks, such as cable networks, the Internet, and wireless networks.User computing devices 104(1)-(N), meanwhile, may be implemented as anynumber of computing devices, including as a personal computer, a laptopcomputer, a portable digital assistant (PDA), a mobile phone, a set-topbox, a game console, a personal media player (PMP), and so forth. Usercomputing device 104(1) is equipped with one or more processors andmemory to store applications and data. An application, such as a browseror other client application, running on device 104(1) may facilitateaccess to provider 106 over network 108.

As illustrated, content provider 106 may be hosted on one or moreservers having processing and storage capabilities. In oneimplementation, the servers might be arranged in a cluster or as aserver farm, although other server architectures may also be used. Thecontent provider is capable of handling requests from many users andserving, in response, various content that can be rendered at usercomputing devices 104(1)-(N) for consumption by users 102(1)-(N).

Architecture 100 also includes a phrase-generation service 110configured to generate and provide phrases for selection by users102(1)-(N). These phrases may be used when users 102(1)-(N) engage intransactions (e.g., with content provider 106) or otherwise. Asdiscussed above, in some instances, user 102(1) may select a phrase forassociation with the user and with one or more aspects of a useraccount, such as a payment instrument of the user or with another useror entity. User 102(1) may then use this phrase to conduct a transaction(e.g., purchase, rent, lease, or otherwise consume content) from contentprovider 106 and/or with other content providers.

As illustrated, phrase-generation service 110 includes one or moreprocessors 112 as well memory 114. Memory 114 includes aphrase-generation module 116, a phrase-suggestion module 118 and aphrase-association module 120. Phrase-generation module 116 functions toanalyze one or more sources 122 to determine phrases and/or to determinewords for creation into phrases. Sources 122 may include books,newspapers, magazines, audio content, video content and/or any othersource from which a corpus of text may be determined. Books may include,for instance, fiction books, non-fiction books, encyclopedias,dictionaries, and any other type of bound or electronic book. Oncemodule 116 has created and/or mined a corpus of phrases, these phrasesmay be stored in a database 124 for suggestion. A database 126,meanwhile, represents that some of these generated phrase may beassociated with users, as discussed below.

Phrase-suggestion module 118 takes phrases from database 124 andsuggests one or more phrases to one or more users. In some but not allinstances, phrase-suggestion module 118 suggests phrases that arepersonalized to the user to whom the phrases are suggested. FIG. 1, forinstance, illustrates that computing device 104(1) of user 102(1)renders a user interface 128 that includes three suggested phrasesprovided by module 118. While user interface 128 may include contentprovided by content provider 106, this interface may also include thephrases suggested by module 118 of phrase-generation service 110.Furthermore, in some instances, some or all of the phrases that userinterface 128 illustrates have been personalized to user 102(1) who, inthe illustrated example, is named Brian. The suggested phrases on userinterface 128 include “Terminator,” “Brian the Plummer,” and “PerceivedLumberjack.” While module 118 visually suggests these phrases to user102(1) in the current example, module 118 may suggest these phrases inany other manner in other implementations (e.g., orally over the phone,in person, via an email or text message, etc.).

FIG. 1 also illustrates that computing device 104(2) of user 102(2)renders a user interface 130. Again, user interface 130 includespersonalized phrases that phrase-suggestion module 118 provides. Here,module 118 suggests the following phrases for user 102(N), whose examplename and city of residence is Susan and Atlanta, Ga., respectively:“Running Tomato,” “Fifth Concerto,” “Absurdly Agile,” and “Sleepless inAtlanta.”

Once user 102(1) or user 102(N) select a phrase, phrase-associationmodule 120 may associate this selected phrase with the correspondinguser. Furthermore, in some instances, phrase-association module 120associates the selected phrase with a payment instrument of the user. Instill other instances, phrase-generation service 110 may not includephase-association module 120. Instead, phrase-generation service maymerely generate and provide phrases for output for a variety of otherreasons, such as for helping users choose an identifier (e.g., for arock band), for helping users write poems or songs, or for any otherreason. Furthermore, while content provider 106 and phrase-generationservice 110 are illustrated in the current example as separate entities,provider 106 and service 110 may comprise the same entity or may employsimilar or the same functionality in other embodiments. Furthermore, thedescribed techniques themselves may be implemented in a vast number ofother environments and architectures.

FIG. 2 illustrates another architecture 200 in which phrase-generationservice 110 may generate and suggest or otherwise provide the generatedphrases. In this example, users 102(1)-(N) select phrases suggested byservice 110 for the purpose of conducting transactions with contentprovider 106. Furthermore, architecture 200 represents thatphrase-generation service 110 may suggest phrases that are personalizedto particular users based on information stored at or accessible bycontent provider 106. For instance, content provider 106 may have accessto previously-known information about user 102(1), which service 110 mayuse to suggest phrases to user 102(1). In still other instances, service110 may personalize these phrases based on information that user 102(1)manually or explicitly provides.

As illustrated, the servers of content provider 106 include one or moreprocessors 202 and memory 204. Memory 204 stores or otherwise has accessto an item catalog 206 comprising one or more items that provider 106may provide to users 102(1)-(N) via a transaction with users 102(1)-(N).An item includes anything that the content provider wishes to offer forpurchase, rental, subscription, viewing, informative purposes, or someother form of consumption. In some embodiments, the item may be offeredfor consumption by the content provider itself, while in otherembodiments content provider 106 may host items that others are offeringvia the provider. An item can include a product, a service, a digitaldownload, a news clip, customer-created content, information, or someother type of sellable or non-sellable unit.

Memory 204 also includes or has access to a user-accounts database 208.Database 208 includes information about users (e.g., users 102(1)-(N))who have a user account at content provider 106 or who have otherwiseengaged in some sort of transaction with provider 106. As discussedabove, service 110 may suggest personalized phrases to users based oninformation stored in or accessible by user-accounts database 208.

An example user account 208(1) is associated with user 102(1) and, asillustrated, aspects of the user account may include a name of the user210, one or more addresses 212 of the user, one or more phrases 214 thathave previously been associated with the user, one or more paymentinstruments 216 of the user that have been associated with account208(1), a purchase history or information about a purchase history 218of the user, items that have been recommended or information about itemsthat have been recommended 220 for the user, and lists or informationabout lists 222 that are associated with the user. Account 208(1) mayalso include one or more specified delivery methods 224 and/or one ormore specified digital locations 226. It is specifically noted thatwhile a few example aspects or elements of user account 208(1) have beenprovided other embodiments may provide more or fewer elements.

Here, user name 210 may specify the legal name of user 102(1) (e.g.,“Brian Brown”). Additionally or alternatively, user name 210 may specifya screen name (e.g., “BBrown18”) that user 102(1) uses whencommunicating with content provider 106. Furthermore, addresses 212 mayinclude a shipping address of user 102(1), a billing address of user102(1), or any other address associated with user 102(1). Again, phrases214 may include any phrases (e.g., “Brian's Fun Cash”) that havepreviously been associated with user account 208(1), as well aspotentially with one or more of the user's associated paymentinstrument(s) 216.

Purchase history 218, meanwhile, may include information about previoustransactions between user 102(1) and content provider 106. Purchasehistory 218 may also, potentially, include information abouttransactions between user 102(1) and other content providers. Thisinformation may include names of items that user 102(1) purchased, tagsthat users or provider 106 has associated with these items (and tagsrelated to those tags), categories in item catalog 206 in which thepurchased items reside, a time of the day or a season when user 102(1)typically purchases items, or any other information evinced explicitlyor implicitly from the user's documented purchase history.

Similarly, recommended items 220 may include any information about itemsthat other users, provider 106, or any other entity has previouslyrecommended for user 102(1). Again, this information may include a nameof the items, categories of the items, tags and related tags that areassociated with the items, and any other information that may be evincedfrom these recommendations. Content provider 106 may store similarinformation about any lists 222 that user 102(1) has created or isotherwise associated with (e.g., names and categories of items on thelists, tags associated with the items and the lists, etc.).

Next, user account 208(1) may specify delivery method(s) 224 selected bythe associated user. These methods may specify that the user prefers tosend and/or receive items via standard mail, express mail, two-day mail,overnight mail, or via any other delivery method. Digital location(s)226, meanwhile, may specify a location where the associated user wishesto receive (and/or send) digital items, such as to a particularcomputing device or location on a server. For instance, if the userpurchases or is gifted a digital item, such as a song, digital location226 may specify a device or destination at which the user would like toreceive the song. Furthermore, digital location(s) 226 may specify wherethe user wishes to receive money that is gifted to the user. Forinstance, this may include a bank account, an online user account, orany physical or electronic location where the user may receive money.

With reference now to phrase-generation service 110, FIG. 2 illustratesthat service 110 may maintain an index 228 that logically maps keywordsto phrases of phrase database 124 (and/or of phrase database 126). Thesekeywords may comprise the words that together comprise the generatedphrases, as well any other word. As illustrated, index 228 maps thekeyword “Aardvark” to “Wily Mammal,” “Burrowing a Shovel,” and “EarthHog,” amongst others. Similarly, index 228 maps the keyword “Apple” to“Newtonian Physics,” “Spinning Core,” and “Funny Feisty Mango,” amongstothers.

As discussed above, phrase-generation service 110 may suggestpersonalized phrases to users based on known information about the usersor based on manual inputs from the users. For instance, content provider106 may provide information about user 102(1) from any of theinformation stored in user account 208(1). Service 110 may then viewthis received information as one or more keywords for insertion intoindex 228. Service 110 may then map the keyword(s) to one or morephrases in the index, which service 110 may then suggest to user 102(1).

For instance, envision that purchase history 218 of user account 208(1)shows that user 102(1) has purchased a large amount of snow-skiingequipment from content provider 106. Envision also that this purchasedequipment falls into categories of the item catalog entitled “sports”and “ski.” Also, the purchased equipment is associated with the tags“Powder Ski” and “downhill.” Upon receiving this information,phrase-generation service 110 may treat the information as keywords forinsertion into index 228. For instance, service 110 may insert some orall of the received words as keywords into index 228 for the purpose ofdetermining phrases that are associated with these keywords.

In some instances, service 110 ranks the returned phrases that areassociated with the inputted keywords. For instance, service 110 mayrank highest the phrases that are associated with each (or most) of thekeywords while ranking lowest the phrases that are associated with onlyone of the keywords. Additionally or alternatively, service 110 may rankthese phrases based on the technique employed to create the phrase, asdiscussed below. In any event, service 110 may output the personalizedphrases to user 102(1), which are likely to relate to skiing given thekeywords inputted into index 228.

While the above example discussed that service 110 could input keywordsassociated with purchase history 218, any other information known aboutuser 102(1) may be similarly inputted. For instance, name 210 or address212 of user 102(1) could be used as a keyword for insertion into index228, as may be any information known about user 102(1), illustrated inuser account 208(1) or otherwise.

Furthermore, phrase-generation service 110 may also provide personalizedphrases based on manual or explicit input from users 102(1)-(N). Forinstance, user 102(1) may provide one or more keywords to service 110,which may then analyze index 228 to determine phrases that areassociated with the provided keywords. For instance, if user 102(1) wereto provide the keyword “Schwarzenegger,” service 110 may return thephrase “Terminator,” amongst others. In still other instances, service110 may provide, to user 102(1), a mix of phrases that are personalizedbased on known information about user 102(1), personalized based onmanually-inputted keywords, and phrases that are not personalized.

Illustrative User Interfaces

FIG. 3 illustrates an example user interface 300 served by contentprovider 106 of FIG. 2. Here, the example user interface includesmultiple phrases 302 that phrase-generation service 110 of FIGS. 1 and 2has generated. Furthermore, service 110 has personalized these generatedand suggested phrases to user 102(1), although in other embodimentsphrases 302 may not be personalized or these phrases may comprise a mixof personalized and non-personalized phrases.

In the current example, content provider 106 provided previously-knowninformation about user 102(1) (from user account 208(1)) to service 110.As discussed above, service 110 inputted this information as keywordsinto index 228 in order to suggest phrases 302. As discussed above, useraccount 208(1) indicates that user 102(1) has previously purchasedskiing items and, as such, may be interested in skiing generally.Phrase-generation service 110 has accordingly entered theseskiing-related keywords into index 228 and has returned phrases 302 thatrelate to skiing (e.g., “Ski Bum,” “Black Diamond,” “Powder,” etc.).

User 102(1) may selected one of suggested phrases 302 or, conversely,may type one or more words into a text box 304 for use as a phrase. Inthe former instance, when user 102(1) selects a phrase from phrases 302,the selected phrase may appear in text box 304. Additionally, the wordsthat compose the selected phrase may themselves be inserted as keywordsinto index 228 of service 110, and the illustrated list of phrases 302may change to reflect phrases that are related to the new keywords, asdiscussed in detail below.

Similarly, if user 102(1) types one or more words into text box 304,then service 110 may again input these words as keywords into index 228.Similar to the discussion above, the list of phrases 302 may againchange to reflect phrases that are related to the newly-inputtedkeywords. Again, the following figures and accompanying discussionillustrate and describe this in detail.

Once user 102(1) has typed or selected a phrase that he or she wishes tokeep, user 102(1) may select an icon 306 (entitled “Add this Phrase toYour Account”) and the phrase that currently resides in text box 304 maybe associated with user account 208(1). In certain embodiments, thisphrase may also be associated with one or more payment instruments(e.g., payment instruments 216 or one or more other payment instruments)as described U.S. Provisional Application No. 60/823,611 and U.S. patentapplication Ser. No. 11/548,111.

In these instances, user 102(1) may use the associated phrase as a proxyfor the associated or linked payment instrument. For example, thisphrase may link with a payment instrument (e.g., a credit card) of user102(1) or some other person or entity. Therefore, user 102(1) (andpotentially one or more other users) may employ this phrase as a paymentmethod for future purchases or other types of transactions.Additionally, user 102(1) (or some other user) may choose to associatecertain rules to the selected phrase. These rules may, for instance,dictate when and how user 102(1) (and, potentially, other users) may usethe phrase for purchases or otherwise. As illustrated, user interface300 includes an area 308 where user 102(1) may decide whether theselected phrase can be used to both place and receive orders, or whetherthe phrase can only be used to receive gifts from other people.

Some rules may dictate which user actually controls the phrase (e.g.,the person associated with the underlying payment instrument, such asthe credit card holder). For instance, a mother may create a phrase forher daughter (“Grace”) entitled “Grace's Textbooks”. Once the mothercreates or approves creation of the phrase, Grace may then specify thisphrase as a payment method. By identifying this phrase as a paymentmethod, the daughter thus identifies the mother (i.e., the personassociated with the linked payment instrument) as the payer for thesepurchases.

Similar to the discussion above, this phrase may be associated withpredefined rules. For instance, the mother may create a rule thatpre-approves purchases of certain categories of items, such astextbooks. The mother may also employ other rules, such as dollaramounts, time limits, and the like. In these instances, when thedaughter uses the phrase as a payment method, the transaction processingservice 110 may compare the parameters of the requested purchase withthe rules associated with the phrase. The service may then complete orcancel the requested purchase according to the phrase's rules.Conversely or additionally, content provider 106 may contact the userthat controls use of the phrase (here, the mother) to request that he orshe approve or deny the requested purchase. The user that controls theuse of the phrase may, in some cases, be requested to authenticatethemselves in some way, such as through a username and password in orderto approve the transaction of the token user. However, because thesephrases, such as phrases 302 shown in user interface 300, may merely bea string of characters that is free from information identifying alinked payment instrument, these phrases may be more freely shared thancompared with other types of payment instrument identifiers, such ascredit card numbers, bank account numbers, and the like.

After user 102(1) selects a rule from area 308, user 102(1) may alsoenter a default shipping address 310 and a default payment method 312for association with the selected phrase. Default shipping address 310specifies a default location where content provider 106 (or othercontent providers) should ship purchased items that user 102(1) (oranother user) purchases with use of the selected phrase. Default paymentmethod 312 similarly specifies a default payment instrument for use inthese transactions. Finally, user interface 300 includes an area 314that allows user 102(1) to choose where a content provider should sendapproval messages in response to these transactions, should an explicitapproval be needed.

FIG. 4 illustrates a user interface 400 served by content provider 106after user 102(1) has typed words (“Trail Running Preferred”) into textbox 304. In response, phrase-generation service 110 has inserted theseentered words as keywords into index 228 and has mapped these keywordsto generated phrases. Service 110 has then provided these phrases 402that are related to the entered keywords for rendering on user interface400. As illustrated, phrases 402 tend to relate to some portion of“Trail Running Preferred,” as they include “Amazingly Swift,” “UnendingMarathon,” “Nervous Shoes” and others.

User interface 400 also includes an indication 404 that the words thatuser 102(1) entered into text box 304 are themselves available as aphrase. That is, user 102(1) may choose to associate the phrase “TrailRunning Preferred” with user account 208(1) via selection of icon 306(“Add this Phrase to Your Account”). Conversely, user 102(1) may chooseto select one of phrases 402 for insertion into text box 304, in whichcase the user could then associate this phrase with user account 208(1)via selection of icon 306. User 102(1) could also choose to select aneven newer set of phrases that are presented in response to theselection of the phrase from phrases 402, and so on.

FIG. 5 illustrates another user interface 500 that content provider 106serves in response to user 102(1) entering “Feilds of Onions” into textbox 304. Again, user interface 500 includes a list of phrases 502 thatrelate to the entered words. However, user interface also includes anindication 504 that the word “feilds” may not be used as a part of aphrase, as this string of characters does not comprise agrammatically-correct English word. As such, user 102(1) may need totype the proper spelling (“Fields”) into text box 304 if user 102(1)wishes to use “Fields of Onions” as a phrase. Of course, while someembodiments require that selected phrases comprise grammatically-correctwords, other embodiments may allow for any string of alphanumericcharacters. In some instances, phrases comprise at least two wordsseparated by a space (as shown in FIGS. 3-5), while in other instancesthese words may not be separated (e.g., “SkiBum”).

FIG. 6 illustrates another user interface 600 that content provider 106serves in response to user 102(1) entering “Brian Brown” into text box304. Similar to the discussion above, user interface 600 includes a listof phrases 602 related to the entered words. However, user interfacealso includes an indication 604 that the phrase “Brian Brown” has beentaken by another user and, as such, user 102(1) may not select thisphrase. As discussed in detail below, phrase-generation service 110 mayalso not suggest or allow selection of a phrase that sounds the same orsimilar to a taken phrase.

FIG. 7 illustrates an example user interface 700 entitled “YourPhrases,” that content provider 106 serves in response to user 102(1)choosing to view his or her phrases 702. As illustrated, user 102(1) hasfive phrases associated with user account 208(1), with each phrase beingassociated with a particular payment instrument. With use of userinterface 700, user 102(1) may choose to edit settings associated withone or more of his or her associated phrases. User 102(1) may alsochoose to “Add a New Phrase” to user account 208(1) via selection of anicon 704.

Illustrative Phrase-Generation Service

FIG. 8 illustrates example components of phrase-generation service 110of FIGS. 1 and 2. As illustrated and as discussed above, service 110includes phrase-generation module 116, phrase-suggestion module 118 andphrase-association module 120. Service 110 may also include aphrase-filtering module 802 and a feedback engine 804.

Phrase-generation module 116 functions to generate phrases, as discussedabove, and store these generated phrases in a first corpus of phrases806 that is stored on or accessible by service 110. Phrase-filteringmodule 802 functions to filter out one or more phrases from the firstcorpus, thus defining a second corpus of phrase 808 that typicallycomprises fewer phrases than first corpus 806. Phrase-suggestion module118 may then suggest phrases of second corpus 808 to user 102(1)-(N),while phrase-association module 120 may associate selected phrases withthese users. Finally, feedback engine 804 may monitor thecharacteristics of the phrases actually being selected by users102(1)-(N) and, in response, cause phrase-suggestion module 118 tosuggest phrases that more closely match these characteristics.

Phrase-generation module 116 may generate first corpus of phrases 806 bya variety of techniques. As illustrated, module 116 includes aphrase-mining module 810 and a phrase-construction module 812.Phrase-mining module 810 functions to mine multiple sources to determinephrases within the sources. Phrase-construction module 812, meanwhile,uses words found within the sources to construct phrases for storage infirst corpus 806. In each instance, the sources from which the modulesdetermine these phrases and words may include books, magazines,newspapers, audio content, video content or any other source that maycomprise a corpus of text.

To find mined phrases, Phrase-mining module 810 includes acontiguous-words module 814 and a statistically-improbable-phrase (SIP)module 816. Contiguous-words module 814 analyzes corpuses of text fromthe sources to find phrases comprising two or more contiguous words. Insome instances, module 814 locates words that are directly contiguousand, as such, are free from separation by punctuation. Module 814 maythen store some or all of the determined phrases within first corpus806. In one embodiment, contiguous-words module 814 locates phrases thatcomprise between two and seven directly-contiguous words.

For example imagine that module 814 mines a book that includes thefollowing sentence: “The night was cold and dreary; the wind howledthrough rattling trees.” Here, module 814 may determine and store thefollowing phrases within corpus of phrases 806:

-   -   The Night    -   The Night Was    -   The Night Was Cold    -   The Night Was Cold And    -   The Night Was Cold And Dreary    -   Night Was    -   Night Was Cold    -   Night Was Cold And    -   Night Was Cold And Dreary    -   Was Cold    -   Was Cold And    -   Was Cold And Dreary    -   Cold And    -   Cold And Dreary    -   And Dreary    -   The Wind    -   The Wind Howled    -   The Wind Howled Through    -   The Wind Howled Through Rattling    -   The Wind Howled Through Rattling Trees    -   Wind Howled    -   Wind Howled Through    -   Wind Howled Through Rattling    -   Wind Howled Through Rattling Trees    -   Howled Through    -   Howled Through Rattling    -   Howled Through Rattling Trees    -   Through Rattling    -   Through Rattling Trees    -   Rattling Trees

As this list illustrates, module 814 may mine phrases comprisingcontiguous words that are not separated by punctuation. By doing so,module 814 is more likely to determine phrases that make grammaticalsense, and each of the phrases comprise words that have been placed nextto one another in an authored work. In instances where the sourcecomprises an edited work, such as book, magazine, or the like, module814 may additionally rely on the fact that a copy editor or the like hasproofread this source and, as such, may determine to a high degree ofcertainty that the determined phrases comprise words that make sensewhen placed next to one another.

Next, SIP module 816 mines one or more sources to determine whether anyof these sources contain one or more statistically improbable phrases(SIPs). Statistically improbable phrases comprise phrases of two or morewords (possibly capped at a number, such as seven) that occur in acorpus of text a statistically improbable number of times. For instance,a biography about Beethoven may contain the phrase “Fifth Concerto” astatistically improbable number of times. As such, SIP module 816 maydetermine that “Fifth Concerto” is a SIP for this biography and, assuch, may store this and other SIPs in first corpus of phrases 806.

Phrase-construction module 812, meanwhile, constructs phrases from wordsand includes a database of templates 818, a part-of-speech (POS)identifier 820 and a class identifier 822. Database 818 may includevarying types of templates, such as part-of-speech templates andpersonalized templates. Each part-of-speech template may comprise one ormore placeholders, with each placeholder specifying a part-of-speech.With this template, phrase-construction module 812 may apply wordshaving the associated part(s)-of-speech to the template to create one ormore phrases. To do so, module 812 includes a part-of-speech (POS)identifier 820 to identify a part of speech for each word mined from asource.

For instance, if module 812 extracts the word “running” from a source,POS identifier 820 determines that this word comprises a verb and, morespecifically, that this word comprises a gerund. With use of these POSor POS/class templates, module 812 may construct some or all of thepossible phrases based on the templates and based on all of the wordsthat module 812 has extracted from sources. Furthermore, becausephrase-generation module 116 may analyze dictionaries, module 812 maycreate all or nearly all of the possible two-word phrases, three-wordphrases, and so on.

With this in mind, envision that database 818 includes the followingtemplate that specifies the following placeholders:

-   -   [verb] [noun]

Phrase-construction module 812 may apply words having associated partsof speech to this template to create one or more phrases. For instance,module 812 may create (and store in corpus 806) phrases such as “RunningChair,” “Dancing Pencil,” “Vault Phone,” “Act Hat.”

Additionally, some or all of the part-of-speech templates withindatabase 818 may include one or more placeholders that specify adescribable class either in lieu of in or in addition to a particularpart of speech. This describable class may specify any sort ofcharacteristic that, when taken together with a part of speech,specifies a subset of the words associated with that particular part ofspeech. As such, phrase-construction module 812 may include a classidentifier 822 that functions to categorize words within respectiveclasses. Class identifier 822 may categorize these words based on liststhat an operator of service 110 has manually entered, by reference tosources such as encyclopedias or dictionaries, or by any other suitablemanner.

With this in mind, envision that database 818 specifies the followingtemplates:

-   -   [gerund that describes movement] [noun that describes a fruit]    -   [noun that describes an occupation] [adverb] [verb that        describes eating]    -   [preposition] the [noun that describes an article of clothing]

With use of the first template, module 812 may construct the followingphrases, among many others: “Walking Mango,” “Flying Pineapple,”“Jogging Orange,” and “Rolling Apple.” With use of the second template,module 812 may construct, among many others, the following phrases:“Seamstress Quickly Bites” and “Plumber Patiently Chews.” Finally, withuse of the third template, module 812 could construct the followingphrases “Beneath the Sweater,” “Against the Clogs,” or “During thePoncho.”

In some embodiments, multiple different part-of-speech and/orpart-of-speech and class combinations may be made based at least in parton the parts of speech listed below in Table 1.

TABLE 1 Abbreviation: Part of Speech: Examples: CC Conjunction, and, orcoordinating CD Adjective, cardinal 3, fifteen number DET DeterminerThis, each, some EX Pronoun, existential there There FW Foreign words INPreposition/Conjunction for, of, although, that JJ Adjective happy, badJJR Adjective, comparative happier, worse JJJS Adjective, superlativehappiest, worst LS Symbol, list item A, A. MD Verb, modal can, could,'ll NN Noun aircraft, data NNP Noun, proper London, Michael NNPS Noun,proper, plural Australians, Methodists NNS Noun, plural women, books PDTDeterminer, prequalifier quite, all, half POS Possessive 's,' PRPDeterminer, possessive mine, yours second PRPS Determiner, possessivetheir, your RB Adverb often, not, very, here RBS Adverb, superlativeFastest RBR Adverb, comparative Faster RP Adverb, particle up, off, outSYM Symbol “ TO Preposition To UH Interjection oh, yes, mmm VB Verb,infinitive take, live VBD Verb, past tense took, lived VBG Verb, gerundtaking, living VBN Verb, past/passive taken, lived participle VBP Verb,base present form take, live VBZ Verb, present 3SG-s takes, lives formWDT Determiner, question which, whatever WP Pronoun, question who,whoever WPS Determiner, possessive & Whose question WRB Adverb, questionwhen, how, however PP Punctuation, sentence ., !, ender PPC Punctuation,comma , PPD Punctuation, dollar sign & PPL Punctuation, quotation “ markleft PPR Punctuation, quotation ” mark right PPS Punctuation, colon, ;,. . . , - semicolon, ellipsis LRB Punctuation, left bracket (, {, + RRBPunctuation, right bracket ), }, +

Furthermore, Table 2 illustrates illustrative parts-of-speech and classcombinations that may be used in creating POS/class templates.

TABLE 2 Combinations: Description: noun.act nouns denoting acts oractions noun.animal nouns denoting animals noun.artifact nouns denotingman-made objects noun.attribute nouns denoting attributes of people andobjects noun.body nouns denoting body parts noun.cognition nounsdenoting cognitive processes and contents noun. communication nounsdenoting communicative processes and contents noun.event nouns denotingnatural events noun.feeling nouns denoting feelings and emotionsnoun.food nouns denoting foods and drinks noun.group nouns denotinggroupings of people or objects noun.location nouns denoting spatialposition noun.motive nouns denoting goals noun.object nouns denotingnatural objects (not man-made) noun.person nouns denoting peoplenoun.phenomenon nouns denoting natural phenomena noun.plant nounsdenoting plants noun.possession nouns denoting possession and transferof possession noun.process nouns denoting natural processesnoun.quantity nouns denoting quantities and units of measurenoun.relation nouns denoting relations between people or things or ideasnoun.shape nouns denoting two and three dimensional shapes noun.statenouns denoting stable states of affairs noun. substance nouns denotingsubstances noun.time nouns denoting time and temporal relations adj.allall adjective clusters adj.pert relational adjectives (pertainyms)adv.all all adverbs verb.body verbs of grooming, dressing and bodilycare verb.change verbs of size, temperature change, intensifying, etc.verb.cognition verbs of thinking, judging, analyzing, doubting verb.communication verbs of telling, asking, ordering, singingverb.competition verbs of fighting, athletic activities verb.consumptionverbs of eating and drinking verb.contact verbs of touching, hitting,tying, digging verb.creation verbs of sewing, baking, painting,performing verb.emotion verbs of feeling verb.motion verbs of walking,flying, swimming verb.perception verbs of seeing, hearing, feelingverb.possession verbs of buying, selling, owning verb.social verbs ofpolitical and social activities and events verb.stative verbs of being,having, spatial relations verb.weather verbs of raining, snowing,thawing, thundering adj.ppl participial adjectives

While Table 1 lists illustrative parts of speech that may be used tocreate templates within template database 818, it is to be appreciatedthat other embodiments may use more or fewer parts of speech. Similarly,while Table 2 lists illustrative part-of-speech/class combinations,other embodiments may use many other such combinations.

With these illustrative tables in mind, in one embodimentphrase-construction module 812 uses the following the parts-of-speechtemplates to construct phrases comprising parts-of-speech and/orparts-of-speech/class combinations:

-   -   [JJ] [NN]    -   [JJ] [NNS]    -   [NN] and [NNS]    -   [NN] and [NN]    -   [RB] [VRB]    -   [VRB] the [NNS]    -   [VRB] the [NN]

In addition to POS and POS/class templates, database 818 may alsoinclude one or more personalized (PRSLZD) templates. These templates mayinclude one or more placeholders and/or one or more fixed or standardwords. For instance, a personalized template may comprise one of thefollowing:

-   -   [user's first name]'s Cash Barrel    -   Sleepless in [user's city of shipping address]    -   I love [keyword associated with user]    -   [keyword associated with user] [user's first name]

With reference to the first template, module 812 could reference useraccount 208(1) to determine the first name of user 102(1). After doingso, module 812 could construct the phrase “Brian's Cash Barrel” forsuggestion and/or association with user 102(1). Similarly, module 812could analyze a user account associated with user 102(N) to determinethat this user's shipping address is in Atlanta, Ga. Module 812 couldthen construct a phrase “Sleepless in Atlanta” for this user. Withreference to the third template, meanwhile, module 812 could construct aphrase “I Love Skiing” for a user who has previously purchased skis.Finally, the module could also construct the phrase “Skier Steve” forthis same user, if his name were Steve.

Returning attention to FIG. 8, once phrase-generation module 116 hascreated first corpus of phrases 806, phrase-filtering module 802 mayfilter out a portion of these phrases to define second corpus 808. Insome instances, phrase-filtering module 802 attempts to filter outless-desirable phrases from users' standpoints to define a corpus of“good” phrases. That is, module 802 attempts to filter out phrases suchthat the remaining phrases are perceived, on the whole, as moreinteresting to most users than compared to the filtered-out phrases. Itis specifically noted that while the illustrated embodiment ofphrase-generation service 110 filters out phrases, in other embodimentsservice 110 may instead positively identify “good” phrases. Furthermore,in some instances, certain phrases of first corpus 806 are not subjectto the filtering process. In one embodiment, SIPs and personalizedtemplates are not processed through at least a portion of the filteringprocess.

As illustrated, phrase-filtering module 802 includes a part-of-speech(POS) filter 824, a blacklist filter 826, a multidimensional filter 828and a whitelist filter 830. Part-of-speech filter 824 functions tofilter out at least a portion of the phrases of first corpus 806 basedon the part-of-speech combinations of the phrases. That is, POS filter824 may include a POS list that either specifies which POS combinationsshould be filtered out or a list that specifies which POS combinationsshould not be filtered out (in which case POS filter 824 may filter outthose phrases whose combination does not fall on the list). In eitherinstance, POS filter 824 removes a great many phrases of first corpus806. Part-of-speech filter 824 may recognize the POS combinations of thephrases with use of existing algorithms and statistical models, such asthe hidden Markov model (HMM).

Table 3, below, lists examples of templates that may be considered“good” POS combinations in some embodiments. Of course, templates thatresult in “good” phrases may vary based on context. In this instance,POS filter 824 may compare each phrase of first corpus 806 and mayfilter out those phrases that do not have a POS combination listed inTable 3.

TABLE 3 CD-NNS IN-NNS IN-PRP JJ-NN JJ-NNS JJ-VBG JJR-NN JJR-NNS JJS-NNJJS-NNS NN-NN NN-NNS NN-POS-NN NN-POS-NNS NNS-POS-NN NNS-POS-NNS NNS-VBNNS-VBD NNS-VBG NNS-VBN NNS-VBP PRPS-NN PRPS-NNS RB-JJ RB-JJR RB-JJSRB-RBR RB-VBD RB-VBG RB-VBN RB-VBZ RBR-NN RBR-NNS RBR-NNS RBR-VBG TO-VBUH-NN VB-NNS VB-RB VB-RB-VB VBD-NN VBD-NNS VBD-RB VBG-JJR VBG-NN VBG-NNSVBG-RB VBN-JJS VBN-NN VBN-NNS VBN-RB VBZ0NN VBZ-RB WPS-NNS CD-IN-CDCD-JJ-NNS CD-JJS-NNS CD-NN-NNS CD-NNS-JJR CD-NNS-RBR CD-VBD-NNSCD-VBG-NNS CD-VBN-NNS DET-JJ-NN DET-JJ-NNS DET-JJS-NN DET-JJS-NNSDET-NN-NN DET-NN- DET-VBG-NN DET-VBG-NNS DET-VBZ- POS-NNS RB-NN JJ-CC-JJJJ-JJ-NN JJ-JJ-NNS JJ-JJ-UH JJ-JJR-NNS JJ-JJR-RB JJ-JJS-NN JJ-NN-NNJJ-NN-NNS JJ-NN-POS-NN JJ-NN-POS-NNS JJ-NN-POS-VB JJ-PRP-VBP-NNJJ-PRP-VBZ-JJ JJ-TO-VBP JJR-CC-JJR JJR-IN-NN JJR-JJ-NN JJR-JJ-NNSJJR-NN-NN JJR-NN-NNS JJR-NN-POS- JJR-NN-POS- JJR-NNS-POS- NN NNS NNSJJR-RB-JJ JJR-RB-RB JJR-TO-NNS JJR-WRB-JJ JJS-CC-JJS JJS-IN-NN JJS-JJ-NNJJS-JJ-NNS JJS-NN- JJS-NN-NNS JJS-NN-POS- JJS-NN-VBP- NN NN VBNJJS-RB-JJ JJS-TO-JJ JJS-TO-VB JJS-VBD-NNS JJS-VBG-NN JJS-VBN-NN MD-NN-VBMD-NNS-NN MD-NNS-VB MD-NNS-VBP MD-PRP-NN MD-PRP-VB MD-RB-RB-JJMD-RB-RB-VBP MD-RB-VB MD-RB-VB-JJ MD-RB-VB- MD-RB-VB- MD-RB-VB-MD-RB-VB- JJR NN NNS VBG MD-RB-VB- MD-RB-VBP MD-RB-VBP- MD-VB-RB- VBN NNVBD MD-VB-RB- MD-VB-VBN- MD-VB-VBN- MD-VB-VBN- VBN CD JJ JJR MD-VB-MD-VB-VBN- MD-VB-VBN- MD-VBP-NNS VBN-NN NNS VBN NN-CC-NN NN-CC-NNSNN-IN-NNS NN-NN-POS- NN NN-POS-CD- NN-POS-JJ- NN-POS-JJR- NN-POS-JJR-NNS NN NN NNS NN-POS-JJS- NN-POS-JJS- NN-POS-NN- NN-POS-RB- NN NNS NN JJNN-POS- NN-POS- NN-POS- NN-POS-VBG- RB-NN VBD-NN VBG-JJR NNS NN-POS-NN-RB-VB-JJ NN-RB-VB- NN-RB-VB- VBN-NN NN NNS NN-VBG-UH NN-VBZ- NN-VBZ-NN-VBZ-RB- NNS RB-JJ VBG NN-WP- NNS-CC- NNS-DET-VBZ- NNS-IN-NNS VBZ-JJNNS NN NNS-JJS-NN NNS-JJS-NNS NNS-MD- NNS-NN-MD- RB-VBP VBD NNS-NN-NNS-NN-VBP- NNS-POS-CD- NNS-POS- POS-NN VBG NNS JJ-NN NNS-TO-VBPNNS-VBG-NN NNS-VBP- NNS-VBP-RB- RB-JJ NNS PRP-JJ-UH PRP-MDNN-PRP-VBD-RB- PRP-VBD- NN NNS RB-VB PRP-VBN- PRP-VBP-JJ- PRP-VBP-PRP-VBP-NN- NNS NNS JJR-JJ NNS PRP-VBP- PRP-VBP-PRPS- PRP-VBP- PRP-VBP-PRPS-NN NNS RB-JJR RB-NNS PRP-VBP-RB- PRP-VBP- PRP-VBZ- PRP-VBZ-RB-JJVBG VBG-NN PRPS-NN PRP-VBZ-RB- PRP-VBZ-RB- PRP-VBZ-RB- PRPS-JJ-NN JJR NNNNS PRPS-JJ-NNS PRPS-JJ-UH PRPS-JJS-NNS PRPS-NN-NNS PRPS-NN-POS-PRPS-NN-POS- PRPS-NNS- PRPS-VBD- NN NNS NN NN PRPS-VBG-NN RB-CC-RBRB-JJ-NN RB-JJ-NNS RB-JJR-VBD RB-JJR-VBN RB-MD-VB-NNS RB-RB-JJ RB-RB-VBNRB-RBR-NN RB-VB-RB- RB-VB-RB- VBN VBP RB-VBD-RB- RB-VBD-RB- RB-VBP-RB-JJRB-VBP-RB- JJ NNS VBN RBR-CC-JJR RBR-IN-NNS RBR-NN-NN TO-VBD-NNTO-VBP-RB UH-IN-JJ UH-NN-POS-NN UH-TO-NN VB-CC-VB VB-DET-NNS VB-DET-UHVB-DET-VBZ- NNS VB-JJ-NNS VB-JJR-NNS VB-NN-NNS VB-NN- POS-NN VB-NN-VB-NNS- VB-NNS-POS- VB-NNS-RB POS-NNS POS-NN NNS VB-NNS-RBR VB-PRP-VB-PRP- VB-PRP- MD-NN VBP-JJ VBP-NN VB-PRP-VBP- VB-PRP- VB-PRPS-NNVB-RB-JJ-RB NNS VBZ-JJ VB-RB-NNS- VB-RB-PRP- VB-RB-RB-JJ VB-RB-RB- VBPVB VBN VB-RB-VB- VB-RB-VBD- VB-RB-VBD- VB-VBN-RBS NNS NN NNS VB-WP-VB-WP-VBZ- VBD-CC- VBD-NN-JJR VBZ-JJ VBN VBD VBD-NNS- VBD-RB-JJ-VBD-RB-JJ- VBD-RB- POS-NNS NN RB VB-JJ VBD-RB- VBG-CC-VBG VBG-CD-NNSVBG-DET-NN VB-NNS VBG-DET- VBG-IN-NNS VBG-JJR-NNS VBG-NN- UH MD-NNVBG-NN-MD- VBG-NN- VBG-NN-POS- VBG-NN- VBD POS-NN NNS VBP-NN VBG-NN-VBG-NNS- VBG-PRP-RBR VBG-PRP- VBP-NN POS-NNS VBP-JJ VBG-PRP-VBP-VBG-PRP- VBG-PRP-VBZ- VBG-PRPS- NN VBZ-JJ NN NN VBG-PRPS- VBG-RB-NNSVBG-RB-RB VBG-RB-VBZ- NNS NNS VBG-RBR- VBG-UH-NN VBG-VB- VBG-VBD- NNSVBZ-NN NNS VBG-VBN- VBG-VBN- VBG-VBZ-NNS VBG-VBZ- NNS RBR RB-JJVBG-WP-VBZ- VBN-CC-VBN VBN-DET-NNS VBN-IN-NNS VBN VBN-JJR-NNSVBN-JJS-NNS VBN-NN- VBN-NN-POS- POS-NN NNS VBN-PRP-JJR VBN-PRP-VBN-PRP-VBZ- VBN-PRPS-NN VBP-JJ NN VBP-RB-NNS- VBP-RB- VBP-RB-PRP-VBP-RB-PRP- JJ PRP-JJ VBD VBG VBP-RB- VBZ-JJ-NNS VBZ-PRP- VBZ-PRP-VBG-NN VBP-JJ VBZ-JJ VBZ-RB- VBZ-RB- VBZ-RB- VBZ-RB- PRP-JJ RB-VB VBG-JJVBG-NN VBZ-RB- VBZ-WP- WDT-VBD- WP-JJ-NN VBG-NNS VBZ-JJ RB-VB WP-JJ-NNSWP-VBZ- WP-VBZ-JJR WP-VBZ- JJ-NN JJR-JJ WP-VBZ-JJS WP-VBZ-NN WP-VBZ-NN-WP-VBZ- POS-NN NN-RB WP-VBZ-NNS WP-VBZ- WP-VBZ- WP-VBZ- PRPS-NN RBS-JJVBG-JJ WP-VBZ- WP-VBZ- WP-VBZ-NN- WP-VBZ- JJS NN POS-NN NN-RB WP-VBZ-WP-VBZ- WP-VBZ- WP-VBZ- NNS PRPS-NN RBS-JJ VBG-JJ WPS-JJ- WPS-JJ-WRB-NN- WRB-NN- NN NNS MD-NN VBP-JJ WRB-NN- WRB-NN- WRB-NNS- WRB-PRP-VBP-VBG VBP-VBN VB VBP-JJ WRB-PRP- WRB-VBZ- WRB-VBZ- WRB-VBZ- VBP-NNSJJ-NN PRPS-NN PRPS-NNS

While Table 3 illustrates a few POS combinations that may be identifiedas good combinations, it is specifically noted that this list is merelyillustrative. Furthermore, because phrases of the first and secondcorpuses may comprise any number of words (one or more), POS filter 824may employ POS lists for each length of phrases. In one particularembodiment, phrases of the first and second corpuses of phrases are twoto seven words in length.

Phrase-filtering module 802 further includes blacklist filter 826.Filter 826 may filter out phrases from first corpus 806 that theblacklist specifies or phrases that include words that appear on theblacklist. For instance, this filter may filter out trademarks, slogans,vulgarities, foreign words, or any other type of word or phrase thatservice 110 wises to specify for exclusion from second corpus 808.

For instance, blacklist filter 826 may filter out phrases that havealready been taken. That is, filter 826 may filter out phrases that havealready been associated with a user. In these instances, filter 826 mayadditionally include a homophone detector 832. Homophone detector 832functions to determine all possible pronunciations of taken phrases andcompare these pronunciations to all possible pronunciations of phrasesthat have not yet been associated with users. If any of thepronunciations sound the same or so similar as to cause confusion, thenfilter 826 may also filer out the phrases that sound similar to thealready-taken phrases.

Multidimensional filter 828, meanwhile, functions to filter out phrasesbased on one or more of a variety of factors. In some instances,multidimensional filter 828 scores each phrase of the first corpus ofphrases based on one or more of these factors. Filter 828 then filtersout those phrases that score below a threshold or may use this scoringin other ways. For instance, filter 828 may use these scores as weightsin order to filter out more phrases having lower scores than comparedwith higher scores.

As illustrated, filter 828 may include a phrase-commonality module 834,a word-commonality module 836, a word-count module 838, a readabilitymodule 840, a scoring module 842 and a ranking module 844.Phrase-commonality module determines, for each phrase of the firstcorpus, a commonality of the phrase. Each determined commonality may berelative to one another and may be based on how often the phrase isfound during the original mining of the multiple sources (e.g., books,magazines, etc.) by phrase-generation module 116. Similarly,word-commonality module 836 may determine a commonality for each word ofeach phrase and assign a score to each phrase based on the determinedword commonalities. Next, word-count module 838 may determine a numberof words that compose each phrase of first corpus 806. Readabilitymodule 840 may determine a readability of the phrase based on, forinstance, a number of syllables in the phrase and/or a number ofsyllables per word of the phrase. In some instances, this readabilityanalysis may be based on indexes such as the Flesch-Kincaid Index or theColeman-Liau Index.

With this information (and potentially other information), scoringmodule 842 may assign a score to each phrase of the first corpus.Ranking module 844 may then rank the phrases based on the determinedscores. Filter 828 may filter out phrases based on the ranking or,conversely, may positively identify phrases to pass through to secondcorpus 808.

Because filter 828 is multidimensional, the scores of the phrases thatare filtered out and/or the scores of the phrases that are passedthrough may be based on experimental data. For instance,phrase-generation service 110 may initially provide phrases to aplurality of test users to determine which phrases those users findinteresting. Envision, for instance, that these users generally finduncommon, easy-to-read phrases comprising two common words to beinteresting, while also finding four-word, difficult-to-read phraseswith uncommon words uninteresting. In this example, ranking module 844may rank much higher phrases that have scores similar to the formerinteresting phrases as compared with phrases that score close to thelatter phrases.

Stated otherwise, the scores that scoring module 842 provides maysegregate the phrases of first corpus 806 into regions. Ranking module844 may then rank discrete regions or may rank these phrases along acontinuous curve. In either instance, phrases failing in regionsdetermined to be generally non-interesting may be filtered out (orweighted less) than phrases falling in regions determined to generallyproduce interesting phrases.

While illustrated filter 828 is shown to score phrases based on phraseand word commonality, word count and readability, other implementationsmay use more or fewer parameters. For instance, in some implementationsfilter 828 factors in a source of the phrase. That is, the filter maytake into account the technique used to generate the particular phrase.For instance, if the testing users appeared to prefer SIPs toconstructed phrase, then scoring module 842 may score SIPs higher thanconstructed phrases. This analysis may also be true within a particulargeneration technique. For instance, if users preferred one POS/classtemplate over another, then constructed phrases built with thisparticular template may score higher than the latter constructed phrases(and, hence, service 110 may suggest these phrases more often).

Other factors that filter 828 may take into account include a longestconsonant run of a phrase, whether the phrase has two or more words thatbegin with the same letter (alliteration), the length of the phrase incharacters, or any other factor that may influence perceivedinterestingness of a phrase to users.

In some embodiments, the most relevant factors of an interestingness ofa phrase include the technique that generated the phrase, thecommonality of the phrase, the commonality of the word(s) of the phrase,the readability of the phrase, and a word count of the phrase. In oneset of testing, the following testing results shown in Table 4 werefound. In these results, the “technique” column represents thegeneration technique, while the “number of observations” represents thenumber of users that rated phrases that fall into the correspondingbucket. The interestingness column, meanwhile, represents the percentageof these phrases perceived as interesting to the testing users.

TABLE 4 Phrase Word Word # of Technique Commonality CommonalityReadability Count Observations Interestingness Contiguous common commonhard 2 139 37% SIP common common easy 2 1839 33% SIP uncommon commoneasy 2 145 32% Constructed common common easy 2 106 32% Contiguouscommon common easy 2 1813 29% Contiguous uncommon common easy 2 241 29%SIP common common hard 2 737 26% Contiguous common common easy 3 112025% Contiguous common common easy 4+ 835 24% Constructed uncommon commoneasy 2 425 24% Constructed uncommon common hard 2 147 24% Constructeduncommon common easy 3 112 22% Contiguous common uncommon easy 2 386 21%SIP common common easy 3 622 20% SIP common uncommon easy 2 851 20%Contiguous uncommon common easy 3 457 20% SIP uncommon common easy 3 18918% Contiguous uncommon common easy 4+ 673 18% SIP common uncommon hard2 673 17% Contiguous common uncommon easy 3 133 17% Constructed uncommonuncommon easy 2 2044 16% Constructed uncommon uncommon hard 2 1304 15%SIP common uncommon easy 3 121 14% Constructed uncommon uncommon easy 3628 14% SIP uncommon uncommon hard 2 267 13% Contiguous uncommonuncommon easy 3 214 13% SIP common common hard 3 242 12% SIP uncommonuncommon easy 2 302 12% Contiguous uncommon uncommon easy 2 144 12%Contiguous uncommon uncommon easy 4+ 161 12% SIP common uncommon hard 3110  5%

In this particular instance, main observations of the testing were thefollowing:

-   -   Constructed phrases may do well if they use common words;    -   SIPs of two words may be more interesting that three-word SIPs;    -   Common phrases containing common words may generally be        perceived as interesting at a relatively high rate;    -   Uncommon phrases with uncommon words may rarely be perceived as        interesting; and    -   Hard-to read phrases that are based on two or more contiguous        words may be deemed as interesting at a relatively high rate.

Furthermore, Table 5 lists four types of phrases that scoring module 842may score well in some instances, as well as example phrases that residewithin these buckets. Of course, it is noted that these types of phrasesmay or may not score well in other instances.

TABLE 5 Attributes Example Phrases Two or more contiguous words,Political Firewall common phrase, common words, Presidential Electionshigh syllables per word, two words Management Prowess Political RealismUnimaginably Violent SIPs, common phrase, common Divine Speaker words,low syllables per word, White Cobra two words Paper Machine Sickly SkinCash Reservoir SIPs, uncommon phrase, common Contrary Grace words, lowsyllables per word, Abstaining Monks two words Therapist Judges ChanceRhyme Crummy Golfers Constructed, common phrase, Purchased Review commonwords, low Focused Cash syllables per word, two words Butter JellyCollected Wealth Makeshift Gear

Returning to FIG. 8, phrase-filtering module 802 also includes whitelistfilter 830. In some instances, filtering module 802 may compare eachword of a phrase to the whitelist filter to ensure that the word appearson a whitelist. If a phrase includes one or more words that do notappear on the whitelist, then filter 830 may filter out these phrases.In some instances, whitelist filter 830 includes each English languageword (and/or potentially one or more other languages). As such, filter830 checks to ensure that phrases being passed through to second corpus808 are indeed English words (or words of the proper language, dependingon context). Additionally, proper names, such as the names of users whohave accounts with content provider 106, may also be added to thiswhitelist.

Once phrase-filtering module 802 has filtered out phrases from firstcorpus 806 to define second corpus 808, phrase-generation service 110may create index 228 that maps keywords to the phrases of second corpus808. As discussed above in regards to FIG. 2, index 228 allowsphrase-suggestion module 118 to provide personalized phrases to a userby inserting into the index keywords that are associated with the user.

In some instances, service 110 may build index 228 in a manner that isreverse from how service 110 will actually use the index. That is,service 110 may create index 228 by mapping each phrase of second corpus808 to one or more keywords. After association of the keywords to thephrases, the index is sorted by keywords such that each keyword maps toone or more phrases. Therefore, when phrase-suggestion module 118 entersone or more keywords into index 228, the index returns one or morephrases for suggestion.

To begin creation of index 228, one or more keywords are associated witha first phrase, such as “Dancing Pencil.” The first keywords that areassociated with this phrase are the words of the phrase itself.Therefore, “Dancing” is associated with “Dancing Pencil,” as is“Pencil.” Next, the stemming variations of those words are related askeywords to the phrase. In the instant example, “Dance,” “Dances,”“Danced,” “Pencils,” and “Penciled” may be associated with “DancingPencil.”

Third, words that are related to “Dancing” and “Pencil” to a certaindegree become associated with “Dancing Pencil.” These related words mayinclude synonyms, hypernyms, and hyponyms. For instance, “pen,”“marker,” and “highlighter” may become associated with “Dancing Pencil,”as may “graphite,” “mechanical,” and “erasable.” In some instances, acompound synonym for a word may also be related to the phrase (e.g.,“non-finite” is a compound synonym of “infinite”).

Next, service 110 searches for sources (e.g., books, magazines, audiocontent, etc.) that is related to the set of words of the phrase itself(here, “Dancing Pencil”). Once these books are found, service 110determines words that are important to the book based on the fact thatthe words appear more than a threshold number of times. Service 110 thenrelates these words to the phrase. For example, “Dancing Pencil” couldbe related to a book having keywords “Ballet” and “Opera” (which appearin the book more than a threshold number of times, for instance). Assuch, service 110 would associate these words with “Dancing Pencil.”

At this point, each phrase within second corpus 808 is associated withone or more keywords. As such, the relationships are turned around toform index 228 such that each keyword of index 228 maps to one or morephrases. Furthermore, service 110 may determine a strength of arelationship between each keyword/phrase combination and may organizeindex 228 accordingly. For instance, a phrase that most strongly relatesto a keyword may appear first in response to entering this keyword intoindex 228. In other instances, service 110 may apply a stronger weightto keyword/phrase relationship based on the ranking, such that thephrases that phrase-suggestion module 118 returns in response to theentering of one or more keywords into index 228 is more likely toinclude phrases having strong relationships with the entered keywords.In still other instances, certain types of phrases (e.g., SIPs) are alsogiven artificially high scores regardless of these phrases' relationshipstrengths to the entered keywords.

Once service 110 has created second corpus 808 and index 228,phrase-suggestion module 118 may suggest or otherwise provide phrases toone or more users. Module 118 may suggest phrases randomly, may suggestphrases from each of multiple buckets, may suggest personalized phrases,or may suggest some combination thereof.

As illustrated, phrase-suggestion module 118 may include a keywordmodule 846 and a pseudo-SIP module 848. Keyword module 846 functions todetermine one or more keywords associated with a user and, in response,determine phrases that are related to the user. Module 118 may thensuggest these personalized phrases to the user. As discussed above,keyword module 846 may determine keywords based on previously-knowninformation about the user or based on manual or explicit input receivedfrom the user.

In either instance, keyword module 846 may insert the determinedkeywords into index 228 to determine related phrases. In instances wherekeyword module 846 inserts more than one keyword into index 228, module846 may analyze index 228 for phrases that are associated with each ofthe entered words. Those phrases that are associated with each of theentered words may be deemed most-related to the entered keywords, whilethose phrases only related to one of the related keywords will be deemedless related. As such, phrase-suggestion module may suggest the mostrelated phrases or may provide more of the heavily-related phrases thanwhen compared to the tenuously- or less-related phrases.

Pseudo-SIP module 848, meanwhile, serves to determine phrases that areassociated with statistically improbable phrases. In some instances,users find SIPs to be particularly interesting phrases. Therefore,Pseudo-SIP module 848 uses the interesting nature of these SIPs in orderto find an even great number of interesting phrases. To do so, module848 may insert the words of one or more SIPs into index 228 in order todetermine phrases that are related to these SIPs (known as “pseudoSIPs). FIG. 10 and an accompanying discussion illustrate and describethis in detail below. Similar to SIPs, phrase-suggestion module 118 mayassign a larger weight to these pseudo SIPs so that module 118 is morelikely to output these types of phrases.

Once module 118 suggests or otherwise provides phrases, users such asuser 102(1) may choose to select a phrase for association with the userand, potentially, with one or more aspects of a user account (such as apayment instrument of the user or the like). In response to receivingselections, phrase-association module 120 assigns the selected phrase tothe user and possibly with one or more aspects of the user account.

Finally, phrase-generation service 110 is shown to include feedbackengine 804. Feedback engine 804 functions to track user selections ofthe phrases, determine characteristics of the phrases being selected,modify the criteria that generates second corpus 808 and that suggestsphrases from second corpus 808 to users. FIG. 11 and an accompanyingdiscussion illustrate and describe feedback engine 804 in detail below.

Illustrative Flow Diagrams

FIG. 9 illustrates an example flow diagram 900 for generating andsuggesting phrases in the manners discussed above. At operation one (1),phrase-generation module 116 analyzes multiple sources to generatemultiple phrases. Module 116 may mine these sources to determine phraseswithin the sources, or module 116 may construct phrases based on wordsfound within these sources.

At operation two (2), phrase-generation module stores these generatedphrases in first corpus of phrases 806. At operation three (3),phrase-filtering module 802 receives phrases from first corpus 806.Phrase-filtering module 802 may filter these phrases via some or all ofthe methods described above. For instance, module 802 may filter thesephrases according to determined part-of-speech combinations, accordingto a blacklist and/or whitelist of words or phrases, and/or based on oneor more other factors. In the latter instance, module 802 may filter outphrases based on an estimated perceived interestingness of the phrases.As such, multidimensional filter 828 may score, rank and filter outphrases based on a phrase commonality, word commonality, readability,and word length, potentially amongst other factors.

At operation four (4), phrase-filtering module 802 provides phrases thathave not been filtered out to define second corpus of phrases 808. Dueto the filtering, second corpus 808 typically comprises fewer phrasesthan first corpus of phrases. Second corpus of phrases 808 may also bemore likely, in some instances, to contain “good” or “interesting”phrases. At operation five (5), phrases of second corpus of phrases 808may be mapped to one or more keywords to create index 228.

At operation six (6), phrase-suggestion module 118 may receive one ormore keywords associated with a particular user for use in suggestingpersonalized phrases for the user. Phrase-suggestion module 118 mayreceive these keywords from information previously know about the user,such as via a user account at content provider 106 or otherwise, ormodule 118 may receive these keywords from the user directly. Forinstance, the user could enter in one or more keywords into userinterface 300 as discussed above in regards to FIG. 3.

Next, operation seven (7) represents that keyword module 846 ofphrase-suggestion module 118 inserts these received keywords into index228. In response, index 228 maps the inserted keywords to one or morephrases and returns these phrases to phrase-suggestion module 118 atoperation eight (8). Finally, at operation nine (9), phrase-suggestionmodule 118 suggests some or all of the received phrases to one or moreusers. In response to receiving a selection of a phrase, phraseassociation module 120 may associate the selected phrase with the userand with one or more aspects of a user account (e.g., a paymentinstrument associated with the user account, a location associated withthe user account (a shipping address, digital location, etc.), adelivery method associated with the user account, etc.). As discussedabove, however, users may use these phrases for other reasons, such asfor use as identifiers of other entities, for help in writing a book ora poem, or in any other manner.

FIG. 10 illustrates an example flow diagram 1000 of a process fordetermining phrases that are related to statistically improbable phrases(i.e., for determining pseudo SIPs). As discussed above, SIPs comprisephrases that occur in a corpus of text a statistically-probable numberof times. Furthermore, users typically rate these phrases as interestingat a higher rate than compared to phrases generated by other techniques.Flow diagram 1000, therefore, attempts to determine more phrases thathave similar characteristics to SIPs.

Flow diagram 1000 includes operation 1002, which represents determiningone or more SIPs that are associated with a particular user. Forinstance, if a user has previously purchased a biography aboutBeethoven, then operation 1002 may determine that the SIP “fifthconcerto” is related to the user. Similarly, if content provider 106recommends the Lord of the Rings book to the user, then operation 1004may determine that the SIP “One Ring” is associated with the user. Inanother example, the user may manually input keywords, such as “HarryPotter,” in which case operation 1002 may determine that the user is nowassociated with the SIP “Lightning Scar.”

Next, operation 1004 determines the words that compose the determinedSIPs. Operation 1006, meanwhile, inserts these determined words askeywords into index 228 to determine phrases that are associated withthese words. In some instances, phrases that are associated with thegreatest number of the input keywords are deemed to be the most related,and so on. If certain phrases are associated with a same number ofinputted keywords, then strengths of these relationships may determinewhich of these phrases is more related to the entered keywords than theother.

Operation 1008 then ranks the phrases that operation 1006 has mapped tothe keywords. In the illustrated example, operation 1006 inserted fourkeywords (W₁, W₂, W₃ and W₄) into index 228. Also as illustrated, index228 maps only a single phrase (P₇) to each of the four keywords. Assuch, operation 1008 ranks this phrase as the most-related phrase. Theother related phrases, meanwhile, are ranked in descending orderaccording to a number of keywords that the phrases map to and, ifnecessary as a tiebreaker, a strength of those relationships. Again,while this represents one illustrative ranking method, otherimplementations may rank the related phrases (i.e., the “pseudo SIPs”)in other manners.

Finally, operation 1010 outputs a portion or all of the determinedphrases for suggestion to the user. As such, this user receives phrasesthat are likely to relate to information in which the user isinterested. These phrases are also likely to contain SIP-like qualities,as Pseudo-SIP module 848 determined these phrases based on SIPs.Operation 1010 may output these phrases randomly, according to theirrank, or otherwise.

While, FIG. 10 illustrates a process for determining phrases that areassociated with statistically improbable phrases, the describedtechniques may similarly determine phrases that are associated with anyone or more random or pre-selected phrases. For instance, the describedtechniques allow for determining phrases that are associated with a setof phrases that share a common characteristic.

To give an example, envision that a user is determined to be associatedwith fruit (e.g., via the user's purchase history or otherwise) and, assuch, the techniques desire to determine phrases that are associatedwith fruit. In a manner similar to the discussion of FIG. 10 above, thetechniques may determine a first set of phrases that are associated withfruit. Next, the words that compose each phrase of the first set ofphrases may be determined. These words may then be inputted as keywordsinto an index (e.g., index 228) that maps keywords to phrases in orderto determine a second set of phrases that are associated with the firstset of phrases.

The phrases that are associated with these keywords (i.e., the phrasesof the second set of phrases) may then be determined and, potentially,ranked. For instance, a phrase that is associated with each inputtedkeyword may be ranked more highly than a phrase that is only associatedwith a single inputted keyword. Finally, phrases of the second set ofphrases may be output for suggestion to a user, for use an identifier orotherwise. In the instant example, the second set of phrases that may beoutput for suggestion to the user will likely somehow relate to fruit,which, again, is a theme associated with the user. As such, thedescribed techniques took a first set of phrases related to fruit andoutputted for suggestion a second set of phrases that are related to thefirst set of phrases (and, as such, are likely related to fruit aswell).

Furthermore, while the first set of phrases may comprise phrasesassociated with a common theme (e.g., fruit), these phrases may shareany other common characteristic. For instance, the first set of phrasesmay comprise phrases that were mined from a common source, that weregenerated in a common manner (e.g., templatized, constructed, mined,etc.), that have the same number of words, or that share any othercommon feature. To give another example, phrases that are mined from acommon source, such as a particular website or webpage, may be enteredinto the index in order to find related phrase. For instance, thetechniques could mine the website to determine phrases that are relatedto the phrases of the website (and, hence, potentially to the websiteitself).

For instance, the techniques could find statistically improbable phrasesfrom the website and/or could determine phrases based on two or morecontiguous words used on the website. The techniques could thendetermine the words that compose these phrases, before determining asecond set of phrases that map to these words. This second set of phrasemay, therefore, represent the content of the website to a certaindegree. Furthermore, as the website (or other source) changes with time,the second set of phrases that relate to the website may also change.

FIG. 11 illustrates an example feedback loop 1100 for learningcharacteristics about the phrases selected by users and, in response tothe learning, altering the characteristics of phrases suggested tousers. As illustrated, at operation one (1) phrases of first corpus ofphrases 806 are inserted into phrase-filtering module 802. As discussedabove, module 802 filters out a portion of these phrases according tomultiple factors, with the phrases that have not been filtered-outdefining second corpus of phrases 808 at operation two (2).

Operation three (3) then suggests some portion of these phrases to eachof multiple users 102(1)-(N). These suggested phrases may comprisebuckets of phrases that have been generated by each generation techniquediscussed above. For instance, the phrases output to users 102(1)-(N)may include mined phrases (both contiguous words and SIPs), constructedphrases (based on both POS templates, POS/class templates andpersonalized templates), pseudo SIPs, and potentially any other form ofgenerated phrase. Some or all of users 102(1)-(N) then select one ormore phrases of these provided phrases.

Next, operation four (4) represents that feedback engine 804 may receivethese user selections of phrases. In response to receiving theseselections, feedback engine may analyze characteristics associated withthe selected phrases. For instance, feedback engine 804 may determinewhich types of phrases users are selecting as well as the details aboutthese selected phrases. For instance, feedback engine 804 may determineif users are selecting more mined phrases or more constructed phrases(or more pseudo SIPs). Within these categories, engine 804 may determinewhich subcategories are popular, and which are not. For instance, engine804 may determine which POS combinations or POS/class combinations usersare selecting, as well as the characteristics of selected SIPs,pseudo-SIPs, and contiguous-word mined phrases.

With this information, feedback engine 804 may determine how the filtersof phrase-filtering module 802 could be tweaked so as to output tosecond corpus 808 phrases that more closely resembles what users102(1)-(N) are actually selecting. Furthermore, feedback engine 804 maydetermine which category and sub-category users are selecting and, inresponse, can cause alteration of the percentage-size of each of thesuggested buckets.

At operation five (5), feedback engine 804 makes a determination thatthe filters of module 802 should be tuned to better reflect the phraseselections of users 102(1)-(N). In response, module 802 tweaks thecorresponding filter and outputs, at operation six (6), a new set ofphrases that now defines a newly-sculpted second corpus of phrases 808.Furthermore, at operation seven (7), second corpus 808 may output a newset of suggested phrases. In some instances, the makeup of thesesuggested phrases varies from the previous set of suggested phrases. Forinstance, these suggested phrases may contain a greater percentage ofphrases generated via a popular technique (e.g., two-word SIPs) and alesser percentage of phrases generated via a less popular technique(e.g., [NN]-[NN] constructed phrases that comprise a common phrase).With use of feedback loop 1100, phrase-generation service 110 is able toquickly and aptly respond to tune the phrases being suggested orotherwise provided to users in order to better suit users' desires.

Furthermore and as discussed above in regards to FIG. 3, users102(1)-(N) may choose to select non-suggested phrases. That is, usersmay choose to create their own phrase. In these instances, feedbackengine 804 will again receive this information at operation four (4).Armed with this information, feedback engine 804 may learn that usersare creating certain types of phrases that second corpus of phrases 808is not providing. Feedback engine 804 may accordingly attempt todetermine how phrase-filtering module 802 could be tweaked to createthese types of phrases.

For instance, engine 804 may determine that a particular POS combinationthat module 802 has been filtering out is actually quite popular amongstusers 102(1)-(N). As such, engine 804 may instruct phrase-filteringmodule 802 to remove this POS combination from a POS-exclusion list ofthe POS filter (or engine 804 may instruct module 802 to add this POScombination to a POS inclusion list). In either instance, phrases ofthis particular POS combination may form a portion of second corpus 808and, as such, may be suggested to users 102(1)-(N) for selection.

Illustrative Processes

FIGS. 12-18 illustrate example processes 1200, 1300, . . . , 1800 forimplementing the techniques described above. While these processes aredescribed with reference to the architectures discussed above, thedescribed techniques may be equally applicable in many otherarchitectures and environments. Furthermore, the described process canbe implemented in hardware, software, or a combination thereof. In thecontext of software, the illustrated operations representcomputer-executable instructions that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular abstract data types. The order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described blocks can be combined inany order and/or in parallel to implement the process.

Process 1200 includes operation 1202, which analyzes multiple corpusesof text to generate a first corpus of phrases. As discussed above, thesecorpuses of text may come from any source, including books, magazines,newspapers, online content, audio content, video content or any othersource. Furthermore, this operation may generate these phrases in any ofthe ways discussed above or otherwise. For instance, this operation maymine phrases, construct phrases, or determine phrases in any way.Operation 1204, meanwhile, represents filtering the first corpus ofphrases to define a second corpus of phrases. This filtering may includefiltering phrases by part-of-speech combinations, according to ablacklist or a whitelist, according to one or more of the multiplefactors discussed above (e.g., commonality, readability, length, etc.),or otherwise.

Next, operation 1206 suggests phrases of the second corpus of phrases toa user. This operation may suggest these phrases visually, orally, or inany other suitable manner. Operation 1208 receives a selection of aphrase, either from the suggested phrases or otherwise. In response,operation 1210 associates the selected phrase with the user and,potentially, with an aspect of a user account of the user. For instance,operation 1208 may associate the selected phrase with a paymentinstrument associated with the user account, a location (e.g., shippingaddress, digital location, etc.) associated with the user account, adelivery method associated with the user account, or the like. In theseinstances, the user may be able to conduct transactions with use of thephrase (and with, for instance, the associated payment instrument,location, or delivery method).

Process 1200 continues to operation 1212, which associates rules to theselected phrase. In some instances, a user to whom the phrase isassociated selects one or more of these rules. Next, operation 1214receives a request to conduct a transaction with use of the selectedphrase. For instance, content provider 106 or another content providermay receive a request from the user to purchase, rent, or otherwiseconsume an item. At operation 1216, the content provider approves therequested transaction if the associated rules allow for the transaction.Operation 1218, however, declines the transaction if the associatedrules do not allow for the transaction.

FIG. 13 illustrates an example process 1300 for suggesting phrases, suchas the phrases generated by the techniques described above. Process 1300includes operation 1302, which represents analyzing multiple corpuses oftext to generate a first corpus of phrases. Next, operation 1304 filtersthe first corpus of phrases to define a second corpus. Operation 1306then determines keywords associated with a particular user. In someinstances, this operation bases this keyword on previously-knowninformation about the user (e.g., name, address, purchase history,etc.), while in other instances this keyword is based on an input fromthe user.

Process 1300 further includes Operation 1308, which representsdetermining phrases that are associated with the determined keywords.This operation may include, for instance, analyzing an index (e.g.,index 228) that maps keywords to phrases. Operation 1310 then suggests,to the user, some or all of the phrases that operation 1308 determinesto be associated with the keyword(s). These suggestions may be in aranked order, beginning with the most-related or may be suggested in anyother manner (e.g., weighted by score, randomly, etc.).

Operation 1312, meanwhile, receives a selection of a phrase from theuser. This selection may be from the suggested phrases or this selectionmay be a user-inputted phrase. Finally, operation 1314 associates theselected phrase with the user and, in some instances, with a paymentinstrument of the user.

FIG. 14 illustrates an example process 1400 for generating phrases bymining multiple sources. Operation 1402 analyzes multiple corpuses oftext to determine statistically improbable phrases. As discussed above,statistically improbable phrase include phrases that appear in a corpusof text more than a predetermined threshold number of times. Next,operation 1404 stores the determined statistically improbable phrases ina first corpus of phrases.

Operation 1406, meanwhile, parses the multiple corpuses of text todetermine phrases comprising two or more contiguous words. In someinstances, these words are free from separation by punctuation (e.g., acomma, a period, etc.). Furthermore, in some instances, these determinedphrases comprise between two and seven words. Next, operation 1408stores the determined contiguous words in the first corpus of phrases.Operation 1410 then filters the first corpus of phrases to define secondcorpus of phrases, while operation 1412 suggests phrases of the secondcorpus to a user. Operation 1414 receives a selection and, in response,operation 1416 associates the selected phrase with the user and with anaspect of a user account of the user (e.g., a payment instrument, alocation, a delivery method, or the like).

FIG. 15 illustrates an example process 1500 for generating phrases viathe construction techniques discussed above. First, operation 1502represents creating a template comprising at least first and secondplaceholders. The first placeholder specifies a first part of speech anda first describable class, while the second placeholder specifies asecond part of speech and a second describable class. The describableclasses may specify any grouping of words that, when associated with apart of speech, specify a subset of words having the specified part ofspeech. For instance, a class may be “words that relate to movement.”When coupled with a verb part of speech, the specified subset of wordsmay include “run,” “dance,” “walk,” and the like. Furthermore, while thefirst and second parts of speech and/or the first and second classes maybe different in some instances, in other instances they may be the same.For illustrative purposes, an example created template may comprise thefollowing placeholders: [a verb in a gerund form that relates tomovement] and [a noun that describes a fruit].

Operation 1504 then analyzes at least one corpus of text to determinemultiple words. For instance, operation 1504 may mine a dictionary orany other source. Next, operation 1506 categorizes the determined words,thereby determining a first word having the specified first part ofspeech and first class and a second word having the second part ofspeech and the second class. In the example template describedimmediately above, for instance, operation 1506 may determine a firstword “dancing” and a second word “bananas.” Operation 1508 then appliesthe determined first and second words to the created template to createa phrase. In the instant example, operation 1508 may create the phrase“dancing bananas.” Finally, operation 1510 associates this phrase with auser and with an aspect of a user account of the user (e.g., a paymentinstrument, a location, a delivery method, or the like) in response toreceiving a selection of this phrase.

FIG. 16 illustrates an example process 1600 for filtering phrases.Similar to the processes described above, operation 1602 representsanalyzing multiple corpuses of text to generate a first corpus ofphrases. Next, operation 1604 determines, for each phrase, a commonalityof the phrase, a commonality of a word of the phrase, a number ofsyllables in the phrase, and a number of words in the phrase. Operation1606 then scores each of the phrases based at least in part on thedetermined values of operation 1604.

Next, operation 1608 then filters the first corpus of phrases to definea second corpus of phrases. As illustrated, operation 1608 representssub operations 1608(1), (2), and (3). Sub-operation 1608(1) removesphrases based on the scoring of the phrases from operation 1606. Next,operation 1608(2) removes phrases that include words appearing on ablacklist of words. Finally, operation 1608(3) removes phrases thatcomprise specified part-of-speech combinations.

Operation 1610, meanwhile, provides phrases of the second corpus ofphrases to a user for selection. In response to receiving a selection ofa phrase, operation 1612 associates the selected phrase with the user.

FIG. 17 illustrates an example process 1700 for determining phrases thatare related to one or more other phrases. These one or more otherphrases may, in some instances, share one or more commoncharacteristics. For instance, these one or more phrases may eachcomprise a statistically improbable phrase (within the same source ordifferent sources). As discussed above, phrases that are determined tobe related to statistically improbable phrases tend to themselves sharecharacteristics of statistically improbable phrases and, as such, areoften perceived as interesting to users.

Process 1700 includes operation 1702, which represents creating an indexthat maps keywords to phrases. For instance, operation 1702 mayrepresent creating index 228 illustrated in FIG. 2. Next, operation 1704determines one or more phrases that are associated with a user. Ininstances where these phrases comprise statistically improbable phrases,operation 1704 may include determining books that are associated withthe user (e.g., via purchase history 218, item recommendations 220,etc.) and determining statistically improbable phrases that appear inthese books. Operation 1706 then determines the words that compose thephrases that are associated with the user.

With use of the words as keywords, operation 1708 analyzes the createdindex to determine phrases that are associated with the keywords. Thismay include, determining phrases that are associated with each of thekeyword, all but one of the keywords, and so forth down to the phrasesthat are only associated with a single inputted keyword. Finally,operation 1710 outputs at least a portion of the determined phrases forsuggestion to a user. In some instances, operation 1710 outputs themost-related phrases to the user, while in other instances operation1710 outputs these phrases randomly or in another manner.

FIG. 18 illustrates an example process 1800 that feedback engine 804 mayimplement. Operation 1802 first represents generating a corpus ofphrases based on a first set of criteria. For instance, this first setof criteria may comprise specified filter parameters, a specifiedweighting of phrases by generation technique, or any other criteria thatmay be used in generating the corpus. Next, operation 1804 provides aportion of the corpus for selection by users. For instance, thisoperation may suggest these phrases via user interface 300, orally, orotherwise.

Operation 1806, meanwhile, receives selections of phrases by users,while operation 1808 analyzes the selected phrases to determine a secondset of criteria. For instance, operation 1808 may determine that userstend to select mined phrases at a higher rate than constructed phrases.Additionally or alternatively, operation 1808 may determine that userstend to select phrases that have a particular score that is based on aphrase commonality, word commonality, readability and word length.

In response, operation 1810 re-generates the corpus of phrases based atleast in part on the second set of criteria. For instance, operation1810 may tweak the filters based on the second criteria and/or may alterthe distribution of provided phrases based on generation technique.After this re-generation, operation 1812 represents providing a portionof the re-generated corpus for selection by users. As discussed above,these phrases may be associated with users in response to received userselections.

Conclusion

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as exemplary forms ofimplementing the claims.

What is claimed is:
 1. One or more non-transitory computer-readablemedia storing computer-executable instructions that, when executed onone or more processors, perform acts comprising: analyzing multiplecorpuses of text to generate a first corpus of phrases, whereinindividual phrases comprise at least two grammatically-correct wordsautomatically generated based at least in part on information associatedwith a user; filtering the first corpus of phrases to define a secondcorpus of phrases comprising fewer phrases than the first corpus ofphrases; suggesting, to the user, phrases of the second corpus ofphrases; receiving a selection of a phrase of the suggested phrases fromthe user; associating the selected phrase with the user and with one ormore of a payment instrument associated with the user, a locationassociated with the user, or a delivery method associated with the user;associating one or more rules to the selected phrase; receiving arequest to conduct a transaction with use of the selected phrase;approving the requested transaction if the associated rules allow forthe requested transaction with use of the phrase; and declining therequested transaction if the associated rules do not allow for therequested transaction with use of the phrase.
 2. One or morenon-transitory computer-readable media as recited in claim 1, whereinthe first corpus of phrases includes statistically improbable phrasesfrom the multiple corpuses of text, phrases that comprise two or morecontiguous words from the multiple corpuses of text, and phrasesconstructed from part-of-speech templates and words from the multiplecorpuses of text.
 3. One or more non-transitory computer-readable mediaas recited in claim 1, further storing computer-executable instructionsthat, when executed on the one or more processors, perform actscomprising: determining a keyword associated with the user; anddetermining phrases that are associated with the keyword, wherein atleast a portion of the suggested phrases comprise the determined phrasesthat are associated with the keyword.
 4. One or more non-transitorycomputer-readable media storing computer-executable instructions for atransaction that, when executed on one or more processors, perform actscomprising: analyzing multiple corpuses of text to generate a corpus ofphrases, at least one of the phrases comprising at least twogrammatically-correct words and being associated with an aspect of auser account; determining one or more phrases of the corpus of phrasesto suggest to individual ones of multiple users, wherein individual onesof the multiple users have a user account and wherein the one or morephrases are automatically generated with information inputted by atleast one of the multiple users; receiving, from a set of the multipleusers, a selection of a suggested phrase; and associating, forindividual users in the set of the multiple users, the selected phrasewith at least one of a payment instrument for executing a transaction oran identity of one of the multiple users.
 5. One or more non-transitorycomputer-readable media as recited in claim 4, wherein the aspect of therespective user account comprises the payment instrument associated withthe user account, a physical or digital location associated with theuser account, or a delivery method associated with the user account. 6.One or more non-transitory computer-readable media as recited in claim4, wherein the at least two grammatically-correct words are separated bya space.
 7. One or more non-transitory computer-readable media asrecited in claim 4, wherein the phrases include words not separated by aspace.
 8. One or more non-transitory computer-readable media as recitedin claim 4, wherein the corpus of phrases includes statisticallyimprobable phrases that appear in at least one of the multiple corpusesof text more than a threshold number of times.
 9. One or morenon-transitory computer-readable media as recited in claim 4 wherein thecorpus of phrases includes phrases comprising two or more contiguouswords within at least one of the multiple corpuses of text.
 10. One ormore non-transitory computer-readable media as recited in claim 4,wherein the corpus of phrases includes phrases that have beenconstructed according to part-of-speech templates.
 11. One or morenon-transitory computer-readable media as recited in claim 4, whereinthe corpus of phrases includes phrases that are constructed by applyingwords to a template.
 12. One or more non-transitory computer-readablemedia as recited in claim 4, wherein the determining comprises filteringout a portion of the corpus of phrases.
 13. One or more non-transitorycomputer-readable media as recited in claim 12, wherein the filteringout comprises filtering the corpus of phrases based at least in part onpart-of-speech combinations of the phrases.
 14. One or morenon-transitory computer-readable media as recited in claim 12, whereinthe filtering out comprises filtering the corpus of phrases based atleast in part on commonality of the phrases, commonality of the wordsthat compose the phrases, readability of the phrases, or a number ofwords of the phrases.
 15. One or more non-transitory computer-readablemedia as recited in claim 12, wherein the filtering out comprisesfiltering out phrases that: (i) include words that appear on ablacklist; (ii) do not appear on a white list of phrases; (iii) havepreviously been selected by a user; or (iv) are homophones of phrasesthat have previously been selected by one or more users.
 16. One or morenon-transitory computer-readable media as recited in claim 4, whereinthe determining of the one or more phrases to suggest to individual onesof the multiple users comprises: determining a keyword associated withindividual ones of the multiple users; and determining phrasesassociated with the keyword.
 17. One or more non-transitorycomputer-readable media as recited in claim 16, wherein the keywordsare: (i) manually inputted by respective users, or (ii) based onpreviously-known information about respective users.
 18. A systemcomprising: one or more processors; and the one or more non-transitorycomputer-readable media storing the computer-executable instructions tostore an application configured to: analyze multiple corpuses of text togenerate a corpus of phrases, at least one of the phrases comprising atleast two grammatically-correct words; determine one or more phrases ofthe corpus of phrases to suggest to individual ones of multiple users,wherein individual ones of the multiple users have a user account andwherein the one or more phrases are automatically generated withinformation inputted by at least one of the multiple users; receive,from a set of the multiple users, a selection of a suggested phrase; andassociate, for individual users in the set of the multiple users, theselected phrase with at least one of a payment instrument for executinga transaction or an identity of one of the multiple users.
 19. One ormore non-transitory computer-readable media storing computer-executableinstructions that, when executed on one or more processors, perform actscomprising: analyzing multiple sources to generate multiple phrases,wherein individual ones of the multiple phrases comprise a string ofalphanumeric characters automatically generated using informationassociated with a user and individual ones of the multiple phrases forassociation with an aspect of a user account used to execute a paymenttransaction; causing at least one phrase of the multiple phrases to besuggested to the user; and responsive to receiving a selection of aphrase from the user, associating the selected phrase with the aspect ofthe user account used to execute a payment transaction.
 20. One or morenon-transitory computer-readable media as recited in claim 19, whereinthe aspect of the user account comprises a payment instrument associatedwith the user account, a physical or digital location associated withthe user account, or a delivery method associated with the user account.21. One or more non-transitory computer-readable media as recited inclaim 19, wherein the payment transaction comprises: (i) purchasing,renting, leasing, or receiving an item, or (ii) sending or receivingmoney.
 22. One or more non-transitory computer-readable media as recitedin claim 19, wherein the multiple sources comprises books, magazines,online content, audio content or video content.
 23. One or morenon-transitory computer-readable media as recited in claim 19, whereineach of the individual strings of alphanumeric characters comprise atleast one grammatically-correct word.
 24. One or more non-transitorycomputer-readable media as recited in claim 19, wherein individualstrings of alphanumeric characters comprise at least twogrammatically-correct words.
 25. One or more non-transitorycomputer-readable media as recited in claim 19, wherein individualstrings of alphanumeric characters comprise at least twogrammatically-correct words that are separated by a space.
 26. One ormore non-transitory computer-readable media as recited in claim 19,wherein at least one of the generated phrases comprises a statisticallyimprobable phrase that appears in one of the sources at least athreshold number of times.
 27. One or more non-transitorycomputer-readable media as recited in claim 19, wherein at least one ofthe generated phrases comprises two or more contiguous words foundwithin one of the sources.
 28. One or more non-transitorycomputer-readable media as recited in claim 19, wherein at least one ofthe generated phrases comprises a phrase that has been constructedaccording to a part-of-speech template.
 29. One or more non-transitorycomputer-readable media as recited in claim 19, wherein at least one ofthe generated phrases comprises a phrase that is based at least in parton a template and personalized for the respective user.
 30. A systemcomprising: one or more processors; and the one or more non-transitorycomputer-readable media storing the computer-executable instructions tostore an application configured to: analyze multiple sources to generatemultiple phrases, wherein individual ones of the multiple phrasescomprise a string of alphanumeric characters automatically generatedusing information associated with a user and individual ones of themultiple phrases for association with an aspect of a user account usedto execute a payment transaction; cause at least one phrase of themultiple phrases to be suggested to the user; and responsive toreceiving a selection of a phrase from the user, associate the selectedphrase with the aspect of the user account used to execute a paymenttransaction.
 31. A method implemented at least in part by a computingdevice, the method comprising: determining, via the computing device,one or more phrases to suggest to a user for association with an aspectof a user account associated with a user, wherein individual ones of theone or more phrases comprise at least one grammatically-correct wordautomatically generated based at least in part on a phrase entered bythe user; receiving a selection of a suggested phrase or a selection ofa user-generated phrase from the user; responsive to the receiving ofthe selection, associating the selected phrase with the aspect of theuser account; receiving, from the user, a request to conduct atransaction with use of the selected phrase; if the requestedtransaction is authorized, completing the requested transaction with useof the selected phrase; and otherwise, declining the requestedtransaction.
 32. A method as recited in claim 31, wherein individualones of the one or more phrases comprises between two and sevengrammatically-correct words.
 33. A method as recited in claim 31,wherein the one or more suggested phrases include statisticallyimprobable phrases, mined phrases, constructed phrases, ortemplate-based phrases.
 34. A method as recited in claim 31, wherein thedetermining of the one or more phrases to suggest to the user comprises:generating a first corpus of phrases; and filtering the first corpus ofphrases to define a second corpus of phrases having fewer phrases thanthe first corpus of phrases.
 35. A method as recited in claim 31,wherein the determining of the one or more phrases to suggest to theuser comprises: determining a keyword associated with the user; andassociating the keyword with the one or more phrases.
 36. A systemcomprising: one or more processors; memory; a phrase-generation module,stored in the memory and executable on the one or more processors togenerate automatically multiple phrases based at least in part on aphrase entered by a user for association with payment instrumentsassociated with users, locations associated with users, or deliverymethods associated with users; a phrase-suggestion module, stored in thememory and executable on the one or more processors to suggest one ormore of the generated phrases to the users; and a phrase-associationmodule, stored in the memory and executable on the one or moreprocessors to associate phrases with payment instruments, locations, ordelivery methods associated with the users in response to receivingselections of suggested phrases from the users.
 37. A system as recitedin claim 36, wherein the generated phrases are between two and sevenwords in length.
 38. A system as recited in claim 36, wherein thephrase-generation module generates the multiple phrases by analyzing oneor more sources that are independent from the payment instruments of theusers.
 39. A system as recited in claim 38, wherein the one or moresources include a book, a dictionary, a magazine, online content, audiocontent, or video content.
 40. A system as recited in claim 36, whereinthe phrase-suggestion module personalizes the suggested one or morephrases based on the respective user to whom the one or more phrases aresuggested.
 41. A system as recited in claim 36, wherein thephrase-generation module generates the multiple phrases by analyzing oneor more sources, and wherein the generated phrases comprisestatistically improbable phrases from the one or more sources, two ormore contiguous words found within the one or more sources, phrasesconstructed according to pre-determined part-of-speech templates, orphrases based on templates that are personalized to a respective user.42. A system as recited in claim 36, further comprising: aphrase-filtering module, stored in the memory and executable on the oneor more processors to filter out a portion of the generated multiplephrases.
 43. A system as recited in claim 42, wherein thephrase-filtering module filters the generated multiple phrases based oncommonality of the phrases, commonality of words that compose thephrases, readability of the phrases, or word counts of the phrases. 44.A system as recited in claim 42, wherein the phrase-filtering modulefilters the generated multiple phrases based on parts-of-speechcombinations of the phrases.
 45. A method implemented at least in partby a computing device, comprising: generating, via the computing device,a corpus of phrases that individually comprise at least onegrammatically-correct word; determining a portion of the phrases fromthe corpus of phrases to suggest to a user based at least in part on akeyword associated with a phrase associated with the user; andresponsive to receiving a user selection of a phrase, associating theselected phrase with an aspect of a user account associated with theuser, the aspect of the user account used to execute a paymenttransaction.
 46. A method as recited in 45, wherein the corpus ofphrases comprises a first corpus of phrases, and wherein determining ofthe portion of the phrases comprises: filtering out one or more phrasesof the first corpus of phrases to define a second corpus of phrases;determining the keyword associated with the user; and determiningphrases of the second corpus of phrases that are associated with thekeyword.
 47. A method as recited in claim 45, wherein the keyword ismanually inputted by the user.
 48. A method as recited in claim 45,wherein the keyword is based on previously-known information about theuser.